Fraud resistant biometric financial transaction system and method

Abstract

A method and system for authenticating financial transactions is disclosed wherein biometric data is acquired from a person and the probability of liveness of the person and probability of a match between the person or token and known biometric or token information are calculated, preferably according to a formula D=P(p)*(K+P(m)) , wherein K is a number between 0.1 and 100, and authenticating if the value of D exceeds a predetermined value.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS[0001]This application is a continuation of, and claims priority to U.S. application Ser. No. 12 / 444,018, filed on Apr. 2, 2009, which is a National Stage Entry of International Application No. PCT / US07 / 80135, filed Oct. 2, 2007, which claims priority to U.S. Provisional Application No. 60 / 827,738, filed Oct. 2, 2006, all of which are hereby incorporated by reference for all purposes.BACKGROUND OF THE DISCLOSURE[0002]This invention relates to biometric identification and authentication systems and methods, more particularly to authentication for financial transactions using biometrics.[0003]Biometric identification and authentication systems are known in the art, for example systems to compare facial features, iris imagery, fingerprints, finger vein images, and palm vein images have been used. Such systems are known to be useful for either comparing biometric data acquired from an individual to stored sets of biometric data of known “enrolled” ...

AI Technical Summary

Benefits of technology

This approach significantly reduces the rejection rate of valid customers and deters fraudulent attempts by obscuring feedback on the success of spoofing methods, thereby enhancing security and customer satisfaction.

Problems solved by technology

For example, the face biometric is easy to acquire (a web camera for example) but it's ability to tell an impostor from an authentic person is somewhat limiting.

Even though some biometrics such as the iris are sufficiently accurate to have no cross-over between the authentics and impostor distributions when the iris image quality is good, if the iris image is poor then there will be a cross-over and the problem reoccurs.

However, in the field of authentication of financial transactions, high levels of accuracy and speed are critical.

In this field, even a small percentage of rejections of authentics can result in an enormous number of unhappy customers, simply because of the huge number of transactions.

In addition, informing the customer (or attempted fraudster) that they successfully got through a biometric system (or not) is not desirable because it enables fraudsters to obtain feedback on methods for trying to defeat the system.

One problem faced by biometric recognition systems involves the possibility of spoofing.

The prior systems and methods have not achieved significant commercial success in the field of authenticating financial transactions due, in part, from the insufficient speed and accuracy from which prior biometric authentication systems for financial transactions suffered.

More specifically, the current methods of basing a decision to perform a financial transaction on the measure of match means that many valid customers are rejected, due to the finite false reject rate.

This makes it much more difficult for an attempted fraudster to refine their fraudulent methods since they are not being provided clear feedback.

However, in some cases where a good customer is trying to perform a transaction and the biometric algorithm is having difficulty performing a match (because light is limited for example and the person's web-cam has a low-contrast image), then the first probability could be low but the second probability could still be high.

In the prior art, false rejects and true accepts are often addressed only in consideration of the biometric match performance, and the substantial business consequences of a false reject is often not considered, and therefore few systems have been implemented practically.

The system results in a large deterrent since in the process of trying to defeat a system, the fraudulent user will have to present some live-person data to the system and they will not know how much or how little live-person data is required to incriminate themselves.

The fraudulent user is also not able to determine precisely how well their fraudulent methods are working, which takes away the single most important tool of a fraudster, i.e., feedback on how well their methods are working.

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